Human-like Competitive Video Game AI Through Reinforcement Learning

With the rise of competitive and multiplayer video games, the demand for non-player characters that can provide meaningful training and practice experiences has increased. With the rise of multiplayer games, developers increasingly require AI-controlled opponents that the players can play against to learn the game, to practice, or to just play by themselves. These AI bots are commonly made with state machines that are manually programmed by programmers. Using state machines for AI players is not only laborintensive but also often results in bots that exhibit predictable and rigid behavior, which can reduce the perception of human-like interaction. In this study, an AI agent was trained using reinforcement learning to play a two-player competitive fighting game, and its behavior was evaluated through gameplay sessions against 17 human participants with varying levels of gaming experience. At the end of our study, the results suggest that training AI agents capable of eliciting a perception of human-like gameplay is feasible within the scope of the studied environment and the integration of the said AI agents is possible through the use of portable technologies.